Generalized Reliability Analysis of Mechanical Systems with Imperfect Maintenance

Generalized reliability models and failure rate models of mechanical systems are developed in this paper according to the system working mechanism, which take the design parameters as input. The models consider strength degradation and imperfect maintenance. Besides, the models take into account the failure correlation caused by homologous load effect and the maintenance correlation owing to group maintenance. Unlike traditional reliability models, the models do not rely on empirical assumptions when considering failure correlation and maintenance correlation and have clear physical meaning. Moreover, the correctness and effectiveness of the models are verified by Monte Carlo simulations. Finally, the influences of failure correlation and maintenance correlation on generalized reliability, the influences of failure correlation on maintenance correlation, and the influences of maintenance correlation on failure correlation are analyzed via numerical examples. The results show that failure correlation and maintenance correlation have great influences on generalized reliability, and the interaction between the two correlation shows obvious time-varying characteristics.

Remaining Useful Life Prediction-Based Maintenance Decision Model for Stochastic Deterioration Equipment under Data-Driven

Currently, the Remaining Useful Life (RUL) prediction accuracy of stochastic deterioration equipment is low. Existing researches did not consider the impact of imperfect maintenance on equipment degradation and maintenance decisions. Therefore, this paper proposed a remaining useful life prediction-based maintenance decision model under data-driven to extend equipment life, promoting sustainable development. The stochastic degradation model was established based on the nonlinear Wiener process. A combination of real-time update and offline estimation estimated the degradation model’s parameters and deduced the equipment’s RUL distribution. Based on the RUL prediction results, we established a maintenance decision model with the lowest long-term cost rate as the goal. Case analysis shows that the model proposed in this paper can improve the accuracy of RUL prediction and realize equipment sustainability.

Progressive maintenance policy for multiple repairable systems with imperfect maintenance

PurposeDegradation of repairable components may not be similar after each maintenance activity; thus, the classic (traditional-time based) maintenance policies, which consider preventive maintenance (PM), age-based maintenance and overhauls to be done at fixed time interval, may fail to monitor the exact condition of the component. Thus, a progressive maintenance policy (PMP) may be more appropriate for the industries that deal with large, complex and critical repairable systems (RS) such as aerospace industries, nuclear power plants, etc.Design/methodology/approachA progressive maintenance policy is developed, in which hard life, PM scheduled time and overhaul period of the system are revised after each service activity by adjusting PM interval and mean residual life (MRL) such that the risk of failure is not increased.FindingsA comparative study is then carried out between the classic PM policy and developed PMP, and the improvement in availability, mean time between failures and reduction in maintenance cost is registered.Originality/valueThe proposed PMP takes care of the equipment degradation more efficiently than any other existing maintenance policies and is also flexible in its application as the policy can be continuously amended as per the failure profile of the equipment. Similar maintenance policies assuming lifetime distributions are available in the literature, but to ascertain that the proposed PMP is more suitable and applicable to the industries, this paper uses Kijima-based imperfect maintenance models. The proposed PMP is demonstrated through a real-time data set example.

Optimal Education Plan of Employees Using Maintenance Model

The employee education is indispensable for companies to improve productive efficiency and product quality. In general, the employee education is divided into two types, i.e., On-job trainings and Off-job ones, and Off-job trainings are divided into two types, i.e., compulsory educations and non-compulsory ones. Compulsory educations such as safety program and compliance education, are necessary to maintain daily production without any accidents. Although all employees of a department and a division gather in a classroom annually in conventional compulsory educations, daily e-learning education complements and strengthens conventional compulsory ones today because employees forget what they learn by annual learning. In past studies, the logit model is used for modeling the influence from education receipt to its memory condition and quantitative relationship between the effect of traffic safety education and the accident-related human-errors was clarified. The effectiveness of the safety driving educational program was indicated by Structural Equation Modelling method. In this paper, an annual compulsory which is complemented and strengthened by e-learning, is discussed. The expected cost rate of education is expressed using imperfect maintenance models and optimal policies which minimizes it is considered. Although we use the exponential function which denotes the occurrence probability of accidents which are caused by forgetting lessons, the actual occurrence probability which is approximated from the actual data would be a complicated one. For solving such complex optimization problems, metaheuristic methods can be applied.

Maintenance optimization of a production system considering defect prevention and spare parts ordering

An innovative maintenance policy is proposed in this paper. This policy can involve spare parts ordering, production quality, and buffer inventory for an efficient production system. In the system, certain batches are required to be produced, and when each batch is finished, a determination is made whether maintenance is needed. The machine state deteriorates with the number of completed batches, and it can be improved by performing maintenance. Two types of maintenance activity, replacement and imperfect maintenance, can be selectively chosen to minimize cost. The defect rate of each batch is related to the number of completed production batches. An innovative concept, defined as the “virtual number” of completed production batches, is used to establish a link between maintenance and defect rate. Monte Carlo simulation and enumerative search is then used to determine cost-effective spare parts ordering and maintenance policies to minimize the cost for the production cycle. Finally, numerical examples are presented to demonstrate the model and to conduct sensitivity analysis. We find that in situations with a high buffer inventory costs, spare parts should be ordered late. When increasing the buffer inventory cost, more replacements should be performed compared to imperfect maintenance. Also, the buffer inventory cost rate and replacement duration time effect the rate of defective products significantly. These two parameters should be kept small, and controlled, if a very low defect rate is needed.